Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time

Justine Chervin, Marc Stierhof, Ming Him Tong, Doe Peace, Kine Østnes Hansen, Dagmar Solveig Urgast, Jeanette Hammer Andersen, Yi Yu, Rainer Ebel, Kwaku Kyeremeh, Veronica Paget, Gabriela Cimpan, Albert Van Wyk, Hai Deng, Marcel Jaspars, Jioji N. Tabudravu

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Abstract

A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.

Original languageEnglish
Pages (from-to)1370-1377
Number of pages8
JournalJournal of Natural Products
Volume80
Issue number5
Early online date26 Apr 2017
DOIs
Publication statusPublished - 26 May 2017

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Biological Products
Streptomyces
Databases
Molecular mass
Molecular Structure
Molecular structure
Screening
Visualization
Color
Pipelines
Throughput
Nuclear magnetic resonance

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Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time. / Chervin, Justine; Stierhof, Marc; Tong, Ming Him; Peace, Doe; Hansen, Kine Østnes; Urgast, Dagmar Solveig; Andersen, Jeanette Hammer; Yu, Yi; Ebel, Rainer; Kyeremeh, Kwaku; Paget, Veronica; Cimpan, Gabriela; Wyk, Albert Van; Deng, Hai; Jaspars, Marcel; Tabudravu, Jioji N.

In: Journal of Natural Products, Vol. 80, No. 5, 26.05.2017, p. 1370-1377.

Research output: Contribution to journalArticle

Chervin, J, Stierhof, M, Tong, MH, Peace, D, Hansen, KØ, Urgast, DS, Andersen, JH, Yu, Y, Ebel, R, Kyeremeh, K, Paget, V, Cimpan, G, Wyk, AV, Deng, H, Jaspars, M & Tabudravu, JN 2017, 'Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time' Journal of Natural Products, vol. 80, no. 5, pp. 1370-1377. https://doi.org/10.1021/acs.jnatprod.6b01035
Chervin, Justine ; Stierhof, Marc ; Tong, Ming Him ; Peace, Doe ; Hansen, Kine Østnes ; Urgast, Dagmar Solveig ; Andersen, Jeanette Hammer ; Yu, Yi ; Ebel, Rainer ; Kyeremeh, Kwaku ; Paget, Veronica ; Cimpan, Gabriela ; Wyk, Albert Van ; Deng, Hai ; Jaspars, Marcel ; Tabudravu, Jioji N. / Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time. In: Journal of Natural Products. 2017 ; Vol. 80, No. 5. pp. 1370-1377.
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abstract = "A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.",
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AU - Chervin, Justine

AU - Stierhof, Marc

AU - Tong, Ming Him

AU - Peace, Doe

AU - Hansen, Kine Østnes

AU - Urgast, Dagmar Solveig

AU - Andersen, Jeanette Hammer

AU - Yu, Yi

AU - Ebel, Rainer

AU - Kyeremeh, Kwaku

AU - Paget, Veronica

AU - Cimpan, Gabriela

AU - Wyk, Albert Van

AU - Deng, Hai

AU - Jaspars, Marcel

AU - Tabudravu, Jioji N.

N1 - Acknowledgment This work was supported in part by the University of Aberdeen Knowledge Transfer Fund grant 032 UZZ0101 (to J.T.). The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013 under grant agreement no. 312184 “PharmaSea” to M.J., R.E., J.T., H.D., J.H.A., and K.Ø.H. R.E., K.K., H.D., and M.J. acknowledge the financial support of the Leverhulme Trust-Royal Society Africa Award (AA090088). J.T., J.C., D.S.U., and M.S. acknowledge Russell Gray of the Spectroscopy Lab., Marine Biodiscovery Centre, University of Aberdeen, for NMR training and help.

PY - 2017/5/26

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N2 - A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.

AB - A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.

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DO - 10.1021/acs.jnatprod.6b01035

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JO - Journal of Natural Products

JF - Journal of Natural Products

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